Description Usage Arguments Value Examples
Given D sequences of test statistics, returns the optimal square rejection that identifies the largest number of simultaneous signals while controlling the false discovery rate. Assumes a common threshold for each sequence.
1 |
T |
n x D matrix of test statistics that are stochastically larger under the null, where n is the number of features and D is the numberof sequences of test statistics |
alpha |
nominal false simultaneous discovery rate |
rho |
regularization parameter to guarantee asymptotic control of the false discovery rate; should be a small positive value, but rho = 0 works well in most simulations |
m |
search for the optimal threshold up to only the mth largest unique value of T; can speed up computation |
rescale |
apply rank transformation to the test statistics within each sequence such that they are of comparable scales |
indices of the features delcared to be simultaneous signals
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## generate paired test statistics
p <- 10^6; ## total number of pairs
X <- c(rep(0,p-30),rep(1,10),rep(2,10),rep(3,10));
## X=0: no signal in either sequence of tests
## X=1: signal in sequence 1 only
## X=2: signal in sequence 2 only
## X=3: simultaneous signal
set.seed(1);
Z1 <- rnorm(p,0,1); Z1[X==1|X==3] <- rnorm(20,3,1);
Z2 <- rnorm(p,0,1); Z2[X==2|X==3] <- rnorm(20,4,1);
T <- cbind(Z1^2, Z2^2);
## rejected simultaneous signals
nfsdr(T, 0.05)
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